Utilizing remote sensing and GIS to detect nutrient stress can help us reduce cultivation costs and increase fertilizer efficiency for crops through site-specific nutrient management. Precision farming technologies can be used to judiciously use water in semi-arid and arid regions. Remote sensing can be used to monitor the health and growth of crops by analyzing spectral data obtained from satellites, airborne sensors, or ground-based instruments. This information can help farmers identify areas of their fields that may need additional attention or water, fertilizer, or pest management. The use of GIS in agriculture enables farmers to map field data, organize and analyze it, and monitor their crops remotely. GPS, robotics, drone and satellite monitoring have all contributed to farm automation. These technologies underpin collecting GIS data. Remote sensing is the use of satellite images that take photos of a field over time so that the grower can analyze conditions based on the data and take action that will have a positive influence on crop yield. Monitoring of vegetation cover for acreage estimation, mapping and monitoring drought condition and maintenance of vegetation health, assessment of crop condition under stress prone environment, checking of nutrient and moisture status of field, measurement of crop evapotranspiration, weed management through precision farming. Remote sensing gives the soil moisture data and helps in determining the quantity of moisture in the soil and hence the type of crop that can be grown in the soil and soil mapping is one of the most common yet most important uses of remote sensing.

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Article Advancement of Remote Sensing for Soil Measurements and Appl...

Thanks

Hi Himanshu Tiwari ,

Numerous practices make agriculture sustainable. Remote sensing could be a useful tool for collecting a lot of information, although this information should be complemented by local information (e.g. drones or ground-based instruments).

By comparing different data sources, the picture will become clearer, making monitoring and forecasting models accurate and reliable.

Just a few examples: ESA - Agriculture